A Unifying Framework for Probabilistic Belief Revision

Zhiqiang Zhuang, James Delgrande, Abhaya Nayak, Abdul Sattar
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
In this paper we provide a general, unifying framework for probabilistic belief revision. We first introduce a probabilistic logic called p-logic that is capable of representing and reasoning with basic probabilistic information. With p-logic as the background logic, we define a revision function called p-revision that resembles partial meet revision in the AGM framework. We provide a representation theorem for p-revision which shows that it can be characterised by the set of basic AGM revision
more » ... basic AGM revision postulates. P-revision represents an "all purpose" method for revising probabilistic information that can be used for, but not limited to, the revision problems behind Bayesian conditionalisation, Jeffrey conditionalisation, and Lewis's imaging. Importantly, p-revision subsumes all three approaches indicating that Bayesian conditionalisation, Jeffrey conditionalisation, and Lewis' imaging all obey the basic principles of AGM revision. As well our investigation sheds light on the corresponding operation of AGM expansion in the probabilistic setting.
doi:10.24963/ijcai.2017/190 dblp:conf/ijcai/ZhuangDNS17 fatcat:qpv5v6nimnertfgdtjf7zojm6y